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115 lines
2.5 KiB
115 lines
2.5 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license
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# COCO 2017 dataset http://cocodataset.org by Microsoft
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# Example usage: python train.py --data coco.yaml
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# parent
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# ├── yolov5
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# └── datasets
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# └── coco ← downloads here (20.1 GB)
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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path: ../datasets/coco # dataset root dir
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train: train2017.txt # train images (relative to 'path') 118287 images
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val: val2017.txt # val images (relative to 'path') 5000 images
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test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
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# Classes
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names:
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0: person
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1: bicycle
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2: car
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3: motorcycle
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4: airplane
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5: bus
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6: train
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7: truck
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8: boat
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9: traffic light
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10: fire hydrant
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11: stop sign
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12: parking meter
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13: bench
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14: bird
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15: cat
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16: dog
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17: horse
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18: sheep
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19: cow
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20: elephant
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21: bear
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22: zebra
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23: giraffe
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24: backpack
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25: umbrella
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26: handbag
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27: tie
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28: suitcase
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29: frisbee
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30: skis
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31: snowboard
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32: sports ball
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33: kite
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34: baseball bat
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35: baseball glove
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36: skateboard
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37: surfboard
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38: tennis racket
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39: bottle
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40: wine glass
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41: cup
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42: fork
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43: knife
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44: spoon
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45: bowl
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46: banana
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47: apple
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48: sandwich
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49: orange
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50: broccoli
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51: carrot
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52: hot dog
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53: pizza
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54: donut
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55: cake
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56: chair
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57: couch
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58: potted plant
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59: bed
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60: dining table
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61: toilet
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62: tv
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63: laptop
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64: mouse
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65: remote
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66: keyboard
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67: cell phone
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68: microwave
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69: oven
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70: toaster
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71: sink
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72: refrigerator
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73: book
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74: clock
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75: vase
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76: scissors
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77: teddy bear
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78: hair drier
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79: toothbrush
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# Download script/URL (optional)
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download: |
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from ultralytics.yoloutils.downloads import download
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from pathlib import Path
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# Download labels
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segments = True # segment or box labels
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dir = Path(yaml['path']) # dataset root dir
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url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
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urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')] # labels
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download(urls, dir=dir.parent)
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# Download data
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urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images
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'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images
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'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional)
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download(urls, dir=dir / 'images', threads=3) |